Feedback and the structure of simulated galaxies at redshift z= 2
Bibliographic record
Abstract
We study the properties of simulated high-redshift galaxies using cosmological Nbody/gasdynamical runs from the OverWhelmingly Large Simulations (OWLS) project. The runs contrast several feedback implementations of varying effectiveness: from no feedback, to supernova-driven winds to powerful active galactic nucleus (AGN)-driven outflows. These different feedback models result in large variations in the abundance and structural properties of bright galaxies at z = 2. In agreement with earlier work, models with inefficient or no feedback lead to the formation of massive compact galaxies collecting a large fraction (upwards of 50 per cent) of all available baryons in each halo. Increasing the efficiency of feedback reduces the baryonic mass and increases the size of simulated galaxies. A model that includes supernova-driven gas outflows aided by the energetic output of AGNs reduces galaxy masses by roughly a factor of 10 compared with the no-feedback case. Other models give results that straddle these two extremes. Despite the large differences in galaxy formation efficiency, the net specific angular momentum of a galaxy is, on average, roughly half that of its surrounding halo, independent of halo mass (in the range probed) and of the feedback scheme. Feedback thus affects the baryonic mass of a galaxy much more severely than its spin. Feedback induces strong correlations between angular momentum content and galaxy mass that leave their imprint on galaxy scaling relations and morphologies. Encouragingly, we find that galaxy discs are common in moderate-feedback runs, making up typically 50 per cent of all galaxies at the centres of haloes with virial mass exceeding 10 11 M . The size, stellar masses and circular speeds of simulated galaxies formed in such runs have properties in between those of large star-forming discs and of compact early-type galaxies at z = 2. Once the detailed abundance and structural properties of these rare objects are well established, it may be possible to use them to gauge the overall efficacy of feedback in the formation of high-redshift galaxies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".